Online medical volume databases, such as those maintained by the National Library of Medicine (NLM), for example, have gained in popularity with the increased use of the Internet. Picture Archiving and Communication Systems (PACS) may be used to support such databases. In addition, the use of three-dimensional imaging modalities that generate volumetric data sets is on the rise, including, for example, Magnetic Resonance Imaging (MRI), Ultrasound (US), Computed Tomography (CT), Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT).
In general, volumetric data sets are massive. For example, the “Visible Male” data set includes axial scans of the entire body taken at 1 mm intervals at a resolution of 512 by 512 pixels. The whole data set has 1870 cross sections, and consumes about 15 GBytes of voxel data. The “Visible Woman” data set includes cross sectional images at one-third the sampling interval of the “Visible Male” along the axial direction, and consumes about 40 GBytes.
When such data sets need to be transmitted over low-bandwidth networks with varying loads and latency constraints, efficient compression schemes must be employed. The compression scheme should support both lossy and lossless compression. Lossy compression allows the user to trade image quality for reduced bit-rates. On the other hand, there are situations where lossless reconstruction is important, such as where small image details might influence the detection of pathology and could alter the diagnosis. The compression scheme should support eight, 12 and 16 bit signed or unsigned data, which is typical of medical images. In general, it should preferably support arbitrary bit-depths.
The benefit of compression can be significantly enhanced if the entire data set does not have to be decompressed prior to visualization. Hence, it is important for the compressed bit-stream to be scalable. Considering that clients are typically limited in display size, the data transmitted by the server should be scalable by resolution. This enables a client to browse a low-resolution version of the volume and appropriately choose a volume of interest (VOI). Distortion scalability is also of interest, so that the VOI of the client is progressively refined by quality.
In addition, scalability by position or spatial location is desired in interactive applications, where interactive users may wish to view a particular sub-section of the volume. Since rendering time is linear in the size of the data set, the compression technology should be based on a multi-resolution framework, with reduced resolution viewing making it possible to save on compressed data transmitted through the network as well as rendering time.
Numerous techniques for image compression have been proposed, many of them supporting some of the scalability constraints mentioned above. Popular techniques as known in the art include embedded zero-tree wavelet coding (EZW) and Set Partitioning in Hierarchical Trees (SPIHT), both of which are wavelet-coding schemes. The JPEG2000 standard, for example, permits the ordering of bits in the compressed data stream to suit the goal.
A common method for visualizing the data set is to use volume rendering. Volume rendering uses a transfer function that maps from voxel intensity values to color and opacity values. What is currently needed is an improved technique for prioritized image visualization from scalable compressed data.